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As a Technical Lead — AI & Analytics at BST Global, you will lead a team of data scientists and data engineers in the design, development, and delivery of machine learning models, data pipelines and analytics products built on Microsoft Azure and Fabric. This role requires deep expertise in ML model architecture and design, data engineering, and proven team leadership skills, including holding staff accountable for deliverables, providing constructive feedback, monitoring work assignments and managing stakeholder expectations.
Job Responsibility:
Lead, mentor, and coach a cross-functional team of data scientists and data engineers
monitor work assignments, track milestones, and hold staff accountable for the quality and timeliness of deliverables
Manage stakeholder expectations by proactively communicating progress, risks, and trade-offs to technical and non-technical audiences
Drive the end-to-end ML lifecycle, including feature engineering, model architecture and design, training, validation, deployment and monitoring
Provide technical guidance on ML model selection, hyperparameter tuning and evaluation metrics
oversee predictive analytics solutions for project management data
Architect scalable, resilient data pipelines using Databricks, Apache Airflow, Microsoft Fabric Data Factory and Microsoft Fabric
lead data modeling and warehousing efforts leveraging medallion architecture and Microsoft Fabric Lakehouse
Establish and enforce engineering standards for ETL/ELT processes, code quality, version control, CI/CD, and security, including row-level and object-level controls
Participate in and lead agile ceremonies
accurately estimate assignments and maintain technical documentation
Evaluate emerging AI/ML frameworks and data engineering tools, making recommendations that advance team capabilities
Assist with interviewing and onboarding new team members to ensure team sustainability
Requirements:
Seven or more years of experience in data engineering and/or data science with at least three years in a technical leadership role overseeing cross-functional data teams
Deep knowledge of ML model architecture and design, including supervised and unsupervised learning, deep learning, NLP, and time-series forecasting
prior experience leading data science teams and translating business problems into analytical solutions
Expert-level understanding of ETL/ELT pipelines, data warehousing, medallion architecture and orchestration tools
prior experience leading data engineering teams building enterprise-scale data platforms
Proven ability to set clear expectations, monitor deliverables, provide constructive feedback and hold team members accountable
skilled at managing stakeholder expectations across technical and business audiences
Strong analytical skills, with the ability to break down complex problems and develop effective solutions
effective at articulating ideas and collaborating across cross-functional teams